class_docstr: Base class for hooks based on summary metrics.
  
  Subclasses should override _process_metrics.
  
  If _process_metrics returns True, calls run_context.request_stop().
  
  This can be used to something like "Stop after the loss has stopped decreasing
  for 5000 steps.
docstr: Process the collected metrics.
  
  Args:
    global_step: int, the current global step value.
    metrics: dict<str subdirectory, dict subdir_metrics>. The collected
      metrics. subdir_metrics is a dict from tag name to tuple of lists. The
      lists are a list of global steps and a list of values.
      i.e. subdir_metrics:
        `dict<str tag, tuple<list<int> global steps, list<float> values>>>`
  
  Returns:
    should_stop: bool. If True, will request that the session stops.
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code: def _process_metrics(self, global_step, metrics):
      """Process the collected metrics.
  
      Args:
        global_step: int, the current global step value.
        metrics: dict<str subdirectory, dict subdir_metrics>. The collected
          metrics. subdir_metrics is a dict from tag name to tuple of lists. The
          lists are a list of global steps and a list of values.
          i.e. subdir_metrics:
            `dict<str tag, tuple<list<int> global steps, list<float> values>>>`
  
      Returns:
        should_stop: bool. If True, will request that the session stops.
      """
      del global_step, metrics
      return False
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 9 for calling the MetricsBasedHook obj's underlying member method code with example usage: obj._process_metrics(global_step, metrics) and returns: False